When ALaM Learns: Machine Learning in Action

When ALaM Learns: Machine Learning in Action

Machine Learning is a new area of research that is gaining ground, and the DOST-ASTI recognizes the importance and impact of this technology to local industry and society in general. With the guidance of Balik Scientist Program recipient, Dr. Jose Ildefonso Rubrico, a research team from the Institute embarked on Project ALaM. ALaM is short for ASTI Labeling Machine, but at the same time, is a play on words referencing the Hiligaynon word, “alam”, which means knowledge.

Project ALaM develops deep learning models, utilizing labeled and georeferenced satellite images from Google Static Maps as input data. The models were “trained” to learn from this large arsenal of satellite images, with the goal of segmenting objects within an image and identifying them. Test images were classified into four classes — agriculture, trees, urban, and water — and achieved 94% accuracy.

The team performed a visual performance evaluation to ascertain the good generality of classification by ALaM. A high-resolution satellite image of Digos City, Davao del Sur was partitioned into several squares. In each square, ALaM executed classification, from which the performances of two deep learning models, based on VGG-16 [University of Oxford] and Inception V3 [Google] architectures, were visually compared.

ALaM serves as proof of concept not only for future application areas, but for further refining of the model. The initial results can be applied in road management and urban planning, and the research team is now looking into improving the model for finer classification; that is, identification of objects in an image such as cars, roads, and trees, among others.